Efficient video quality assessment for on-demand video transcoding using intensity variation analysis

Abstract

Due to the wide spread usage of smart devices, adopting video contents service to the diverse end user’s service environment is an essential process. The heterogeneity of end users’ devices, usually referred as the device fragmentation, requires video transcoding which is a lossy process. Accordingly, the subsequent video quality degrading is inevitable. In such circumstances, minimizing perceptible quality loss of video is a key issue for the video contents service provider. However, the video quality loss caused in the process of transcoding is very difficult to measure. Because the “video quality” is a subjective term, it is almost impossible to estimate before video contents are delivered and actually serviced. To address this issue, many research efforts have been pursued for estimating subjective quality evaluation score using objective quality assessment metric. Structural Similarity (SSIM) is a well-known objective quality assessment method. Based on previous studies, this method has been used as a very effective quality assessment tool in video coding system. In this paper, we propose new video quality assessment metric using intensity variation analysis. The intensity metric-based video quality assessment has a high correlation with the SSIM regardless of the category of video contents, resolutions and even bitrate setting. The proposed method that measures inter-frame intensity variation (IV) is more efficient than SSIM in VBR transcoding system. Our experimental results show that the proposed video quality assessment shows up to 22 times faster than SSIM in the execution time. Ultimately, to take its advantage of the short latency and low execution overhead, IV-based video assessment is applicable to real on-demand transcoding and streaming environments while minimizing video quality degradation of transcoding.